



'''simple docstring'''

import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


SCREAMING_SNAKE_CASE__     :						int	=  [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['''memory_attention''', '''encoder_attn'''],
    ['''attention''', '''attn'''],
    ['''/''', '''.'''],
    ['''.LayerNorm.gamma''', '''_layer_norm.weight'''],
    ['''.LayerNorm.beta''', '''_layer_norm.bias'''],
    ['''r.layer_''', '''r.layers.'''],
    ['''output_proj''', '''out_proj'''],
    ['''ffn.dense_1.''', '''fc2.'''],
    ['''ffn.dense.''', '''fc1.'''],
    ['''ffn_layer_norm''', '''final_layer_norm'''],
    ['''kernel''', '''weight'''],
    ['''encoder_layer_norm.''', '''encoder.layer_norm.'''],
    ['''decoder_layer_norm.''', '''decoder.layer_norm.'''],
    ['''embeddings.weights''', '''shared.weight'''],
]






def 				a  (     UpperCamelCase_    :    Optional[Any]			)       ->	List[Any]:
		for pegasus_name, hf_name in PATTERNS:
				snake_case__	     =k.replace(UpperCamelCase_      ,					UpperCamelCase_			)
		return k






def 				a  (     UpperCamelCase_    :    dict      ,					UpperCamelCase_    :    dict			)       ->	PegasusForConditionalGeneration:
		snake_case__	     =DEFAULTS.copy()
		cfg_kwargs.update(UpperCamelCase_			)
		snake_case__	     =PegasusConfig(**UpperCamelCase_			)
		snake_case__	     =PegasusForConditionalGeneration(UpperCamelCase_			)
		snake_case__	     =torch_model.model.state_dict()
		snake_case__	     ={}
		for k, v in tf_weights.items():
				snake_case__	     =rename_state_dict_key(UpperCamelCase_			)
				if new_k not in sd:
						raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})"""			)

				if "dense" in k or "proj" in new_k:
						snake_case__	     =v.T
				snake_case__	     =torch.tensor(UpperCamelCase_      ,					dtype=sd[new_k].dtype			)
				assert v.shape == sd[new_k].shape, f"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
		# make sure embedding.padding_idx is respected
		snake_case__	     =torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]			)
		snake_case__	     =mapping['shared.weight']
		snake_case__	     =mapping['shared.weight']
		snake_case__	     ={k: torch.zeros_like(UpperCamelCase_			) for k, v in sd.items() if k.endswith('bias'			) and k not in mapping}
		mapping.update(**UpperCamelCase_			)
		snake_case__     ,	snake_case__	     =torch_model.model.load_state_dict(UpperCamelCase_      ,					strict=UpperCamelCase_			)
		snake_case__	     =[
		    k for k in missing if k not in ['encoder.embed_positions.weight', 'decoder.embed_positions.weight']
		]
		assert unexpected_missing == [], f"""no matches found for the following torch keys {unexpected_missing}"""
		assert extra == [], f"""no matches found for the following tf keys {extra}"""
		return torch_model






def 				a  (     UpperCamelCase_    :    List[str]="./ckpt/aeslc/model.ckpt-32000"			)       ->	Dict:
		snake_case__	     =tf.train.list_variables(UpperCamelCase_			)
		snake_case__	     ={}
		snake_case__	     =['Adafactor', 'global_step']
		for name, shape in tqdm(UpperCamelCase_      ,					desc='converting tf checkpoint to dict'			):
				snake_case__	     =any(pat in name for pat in ignore_name			)
				if skip_key:
						continue
				snake_case__	     =tf.train.load_variable(UpperCamelCase_      ,					UpperCamelCase_			)
				snake_case__	     =array
		return tf_weights






def 				a  (     UpperCamelCase_    :    str      ,					UpperCamelCase_    :    str			)       ->	List[Any]:
		# save tokenizer first
		snake_case__	     =Path(UpperCamelCase_			).parent.name
		snake_case__	     =task_specific_params[f"""summarization_{dataset}"""]['max_position_embeddings']
		snake_case__	     =PegasusTokenizer.from_pretrained('sshleifer/pegasus'      ,					model_max_length=UpperCamelCase_			)
		assert tok.model_max_length == desired_max_model_length
		tok.save_pretrained(UpperCamelCase_			)

		# convert model
		snake_case__	     =get_tf_weights_as_numpy(UpperCamelCase_			)
		snake_case__	     =task_specific_params[f"""summarization_{dataset}"""]
		if dataset == "large":
				snake_case__	     =task_specific_params
		snake_case__	     =convert_pegasus(UpperCamelCase_      ,					UpperCamelCase_			)
		torch_model.save_pretrained(UpperCamelCase_			)
		snake_case__	     =torch_model.state_dict()
		sd.pop('model.decoder.embed_positions.weight'			)
		sd.pop('model.encoder.embed_positions.weight'			)
		torch.save(UpperCamelCase_      ,					Path(UpperCamelCase_			) / 'pytorch_model.bin'			)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE__     :						Optional[int]	=  argparse.ArgumentParser()
			# Required parameters
			parser.add_argument('''tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
			parser.add_argument('''save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
			SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  parser.parse_args()
			if args.save_dir is None:
						SCREAMING_SNAKE_CASE__     :						List[Any]	=  Path(args.tf_ckpt_path).parent.name
						SCREAMING_SNAKE_CASE__     :						Dict	=  os.path.join('''pegasus''', dataset)
			convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)









'''simple docstring'''

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def 				a  (     UpperCamelCase_    :    Any			)       ->	List[str]:
		snake_case__	     =os.path.join(args.tf_model_dir      ,					'parameters.json'			)
		snake_case__	     =json.loads(open(UpperCamelCase_			).read()			)
		if not params:
				raise ValueError(
				    f"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""			)
		if not args.output.endswith('.pt'			):
				snake_case__	     =args.output + '.pt'
		snake_case__	     =OrderedDict()
		with tf.device('/CPU:0'			):
				snake_case__	     =tf.train.load_checkpoint(args.tf_model_dir			)
				snake_case__	     =reader.get_variable_to_shape_map()
				for key_name in shapes.keys():
						snake_case__	     =reader.get_tensor(UpperCamelCase_			).astype(np.floataa			)
						if key_name.endswith('/adam_m'			) or key_name.endswith('/adam_v'			):
								continue
						if key_name.startswith('pasts/'			):
								if key_name.startswith('pasts/mlp'			):
										snake_case__	     =int(key_name[9]			)
								elif key_name.startswith('pasts/out'			):
										snake_case__	     =8
								snake_case__	     ='model.sqout.%d.weight' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
								snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
								snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/moe'			):
								snake_case__	     =int(key_name[9:].split('/'			)[0]			)
								if key_name.endswith('/switch_gating/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/softmlp/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/wo/kernel'			) or key_name.endswith('/wi/kernel'			):
										snake_case__	     =key_name[-9:-7]
										for i in range(16			):
												snake_case__	     ='model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
												snake_case__	     =(
												    vnp[i].transpose([1, 0]			).copy()
												)  # In Mesh-Tensorflow, it is one array, so it is divided
												snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/mlp'			):
								snake_case__	     =int(key_name[9:].split('/'			)[0]			)
								if key_name.endswith('/p1/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wi.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/p1/bias'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wi.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/p2/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wo.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/p2/bias'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wo.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/ln'			):
								snake_case__	     =int(key_name[8:].split('/'			)[0]			)
								if key_name.endswith('/b'			):
										snake_case__	     ='model.blocks.%d.feed_forward.norm.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/g'			):
										snake_case__	     ='model.blocks.%d.feed_forward.norm.weight' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/att'			):
								snake_case__	     =int(key_name[9:].split('/'			)[0]			)
								if key_name.endswith('/qkv/kernel'			):
										snake_case__	     =vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
										snake_case__	     =state[:, 0, :, :]
										snake_case__	     =state[:, 1, :, :]
										snake_case__	     =state[:, 2, :, :]
										snake_case__	     =(
										    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]			)
										    .transpose([1, 0]			)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =(
										    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]			)
										    .transpose([1, 0]			)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =(
										    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]			)
										    .transpose([1, 0]			)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
										snake_case__	     =torch.tensor(UpperCamelCase_			)
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
										snake_case__	     =torch.tensor(UpperCamelCase_			)
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/o/kernel'			):
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
										snake_case__	     =(
										    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]			).transpose([1, 0]			).copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/an'			):
								snake_case__	     =int(key_name[8:].split('/'			)[0]			)
								if key_name.endswith('/b'			):
										snake_case__	     ='model.blocks.%d.self_attn.norm.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/g'			):
										snake_case__	     ='model.blocks.%d.self_attn.norm.weight' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif (
						    key_name.startswith('model/wte'			)
						    or key_name.startswith('model/wpe'			)
						    or key_name.startswith('model/ete'			)
						):
								snake_case__	     ={'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
								    key_name[-3:]
								]
								snake_case__	     ='model.%s.weight' % nlayer
								snake_case__	     =vnp.copy()  # same in embedded
								snake_case__	     =torch.tensor(UpperCamelCase_			)
								if key_name.startswith('model/wte'			):
										snake_case__	     ='lm_head.weight'
										snake_case__	     =vnp.copy()  # same in embedded
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/wob'			):
								snake_case__	     ='final_logits_bias'
								snake_case__	     =vnp.copy()  # same in embedded
								snake_case__	     =state.reshape((1, -1)			)
								snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name == "model/dense/kernel":
								snake_case__	     ='model.last_project.weight'
								snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
								snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name == "model/dense_1/bias":
								snake_case__	     ='model.last_project.bias'
								snake_case__	     =vnp.copy()  # same because it is one dimensional
								snake_case__	     =torch.tensor(UpperCamelCase_			)
		torch.save(UpperCamelCase_      ,					args.output			)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE__     :						int	=  argparse.ArgumentParser(
			    description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
			)
			parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
			parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
			SCREAMING_SNAKE_CASE__     :						str	=  parser.parse_args()
			convert_tf_gptsan_to_pt(args)




